Detecting early signs of problems in a complex plant operation is tantamount to preventing an interruption in the manufacturing/production processes. It is desirable for a production plant to maintain a steady state operational trend (e.g., hydrogen production facility). Practically, however, day to day changes in the production plant will affect plant operations and often will stress plant operations (e.g., increase production yield to meet customer demand, raw material feed composition variations, and even weather conditions can affect the plant operation). Another contributory factor that renders steady state production a challenge and renders fault detection more difficult is the inherent nature of a cyclic but asynchronous production system (e.g., PSA production systems).
A number of methods and systems are described in the prior art to address potential problems in production plants by adjusting process variables based on changes in measured process parameters. For example, U.S. Pat. No. 8,016,914, Belanger et al., U.S. Pat. No. 7,674,319, Lomax et al., and U.S. Pat. No. 7,789,939, Boulin, teach various methods for measuring an impurity and adjusting a process variable, such as feed time, to control that impurity in a bed of a PSA system. Such single bed PSA control is widely used and has become an industry practice.
Other production plant fault detection methods have been discovered and implemented. For example, as is described in the article, entitled, “Finding the Source of Nonlinearity in a Process With Plant-Wide Oscillation”, Nina F. Thornhill, 2005, Thornhill proposes a non-linearity index that can be used to detect a root cause of oscillation for a dynamic system having a plurality of interacting control loops. This method can be used to detect oscillations caused by self-sustained limit cycles in a control loop. Such oscillations often originate in one loop but propagate to the other loops. With this current practice, the developed non-linearity metric produces high values for the source control loop and lower values for the secondary oscillations that allow a root cause analysis to be performed. The method is based on comparison of surrogate data and real plant data. With this current practice, surrogate data is obtained by applying a discrete Fourier transform (DFT) to real data and then randomizing the arguments of DFT and keeping the amplitude constant. Subsequent in the method, an inverse DFT is applied to produce the surrogate data. The real data with phase coupling produces more structured and more predictable trends than surrogate data. Accordingly, with this existing practice, the non-linearity index exploits the difference in data meaning using time series analysis. Thornhill discloses a method that is practically used for detecting plant-wide oscillation due to interacting control loops and helps to tune controllers for the optimum plant performance.
Existing practices fall short, however, in identifying which step of a cyclical asynchronous production process is the root cause of a generally observed production fault or problem. Although current practices described in prior art utilize a DFT to generate surrogate data for use in identifying process failures, they fail to address various key issues such as the processing of steady state production plant variables. Specifically, with production cycle fluctuations, the variables can either have a basis from “self sustained oscillation” due to a primary/secondary effect of a control loop or coming from noise. It would be advantageous to have a detection system and method that handles cyclic steady state data where oscillation is the normal operation. Such systems and methods could operate to compare oscillation characteristics across various portions of production lines in a production plant (e.g., various production “beds” of a PSA plant) where production line portions among similar production lines would operate to have a similar oscillation with different phase (e.g., multi-bed PSA production plant having asynchronous production steps among a plurality of production lines). Furthermore, it would be advantageous to have a monitoring system and method applied continuously and automatically across a production process throughout the entirety of a steady state production plant.
By way of example, an advantageous system and method could operate in the context of a PSA plant with measuring bed-to-bed variation, and relating those variations to processes inside and outside of the PSA process itself, such as feed composition change, plant production step abnormalities, and/or operational deficiencies such as broken equipment (e.g., a broken valve in the PSA system itself). The desired method could provide steps to detect any deviation in an out-of-phase cyclic system (e.g., PSA) with a number of subunits (e.g., beds). The goal is to ensure that each unit behaves identically to all others when transposed to the same phase. The out-of-phase cyclic system (e.g., PSA) itself, in turn, is affected operationally by other processes in the plant and the desirous systems and methods would account for such environmental and operational variables.
Therefore, there is a need for systems and methods to monitor and analyze data surrounding the execution of a normally cyclic but asynchronous system together with a normally steady state production process.